Knowledge Commons of Institute of Automation,CAS
IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation | |
Yan, Lan1,2![]() ![]() ![]() | |
发表期刊 | KNOWLEDGE-BASED SYSTEMS
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ISSN | 0950-7051 |
2022-04-06 | |
卷号 | 241页码:11 |
摘要 | Photo-to-caricature translation is an extremely challenging task because there are not only texture differences between caricatures and photos, but also various spatial deformations in caricatures. Most of existing methods tend to introduce difficult obtained additional information such as precise facial landmarks to guide caricature generation. In addition, identity preservation is a crucial characteristic of caricatures, but unfortunately there seems to be few methods to consider it. Motivated by the aforementioned observations, we propose an Identity-Preservation Generative Adversarial Network (IPGAN) for unsupervised photo-to-caricature translation. In particular, considering the importance of identity retention, we propose a novel identity preservation loss to hold the identity information of original photos and improve the quality of generated caricatures. To capture realistic caricature styles, we design a style differentiation loss to help our model produce caricatures with styles that remarkably differ from photos. Moreover, to learn satisfactory deformations without supervision, our model uses a warp controller to acquire exaggerations automatically that enable to customize diverse exaggerations. As an unsupervised translation method, our IPGAN can also be applied to caricature to-photo translation. Experiments on the WebCaricature dataset suggest that our IPGAN achieves state-of-the-art performance and can generate realistic as well as identity preservation caricatures. |
关键词 | Photo-to-caricature translation Generative adversarial networks Image-to-image translation Style transfer Image warping |
DOI | 10.1016/j.knosys.2022.108223 |
关键词[WOS] | IMAGE ; FACES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2018AAA0101502] ; Key Research and Devel-opment Program of Guangzhou, China[202007050002] ; Natural Science Foundation of China[61806198] ; Natural Science Foundation of China[U1811463] |
项目资助者 | National Key R&D Program of China ; Key Research and Devel-opment Program of Guangzhou, China ; Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000788730900008 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48441 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Gou, Chao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 3.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China 4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yan, Lan,Zheng, Wenbo,Gou, Chao,et al. IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation[J]. KNOWLEDGE-BASED SYSTEMS,2022,241:11. |
APA | Yan, Lan,Zheng, Wenbo,Gou, Chao,&Wang, Fei-Yue.(2022).IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation.KNOWLEDGE-BASED SYSTEMS,241,11. |
MLA | Yan, Lan,et al."IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation".KNOWLEDGE-BASED SYSTEMS 241(2022):11. |
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IPGAN: Identity-Pres(3723KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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